Ding, Zijuan, Liu, Ke, Grunwald, Sabine, Smith, Pete, Ciais, Philippe, Wang, Bin, Wadoux, Alexandre M.J.‐C., Ferreira, Carla, Karunaratne, Senani, Shurpali, Narasinha, Yin, Xiaogang, Roberts, Dale, Madgett, Oli, Duncan, Sam, Zhou, Meixue, Liu, Zhangyong, and Harrison, Matthew Tom (2025) Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process‐Based and Hybrid Modelling Approaches. Advanced Science Letters, 12. e04152.

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Abstract

Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo-climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI-driven SOC prediction improves when RS covariates are included. Although DL often outperforms classical ML, there is no single best AI algorithm. By incorporating simulated outputs from biogeochemical model as additional training data for AI, causal relationships in SOC turnover can be incorporated into empirical modelling, while maintaining predictive accuracy. In conclusion, SOC prediction can be enhanced through 1) integrating sensing technologies, 2) applying AI, notably DL, 3) addressing biogeochemical model limitations (assumptions, parameterization, structure), 4) expanding SOC data availability, 5) improving mathematical representation of microbial influences on SOC, and 6) strengthening interdisciplinary cooperation between soil scientists and model developers.

Item ID:	94085
Item Type:	Article (Research - C1)
ISSN:	1936-7317
Copyright Information:	© 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date Deposited:	01 Oct 2026 06:07
FoR Codes:	41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410604 Soil chemistry and soil carbon sequestration (excl. carbon sequestration science) @ 100%
SEO Codes:	18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180601 Assessment and management of terrestrial ecosystems @ 100%
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